Monitor Your NVIDIA GPUs from the Browser with GPU-Hot

Ever find yourself tabbing out of your game or stopping your training script just to check GPU stats? Or maybe you're managing a headless server and want a quick, real-time look at your hardware without SSH. If you've got NVIDIA GPUs, there's a new tool that makes monitoring them as easy as opening a web browser.

Enter GPU-Hot, a lightweight, real-time web monitoring panel. It's a local web server that pulls data directly from your NVIDIA GPUs using the nvidia-smi command and serves it up in a clean, auto-updating dashboard. No cloud, no complicated setup—just local stats, fast.

What It Does

GPU-Hot is a simple Python application that runs a local web server on your machine. It periodically queries your NVIDIA GPU(s) for key metrics like temperature, utilization, memory usage, power draw, and fan speed. It then formats this data into a clean, real-time web interface that updates every second. Think of it like a browser-based watch nvidia-smi, but with a persistent, visual dashboard.

Why It's Cool

The clever part is its simplicity and directness. It doesn't rely on a heavy graphics stack or complex dependencies. It uses the system's built-in nvidia-smi tool, wraps it in a minimal Flask server, and pushes updates to the frontend using Server-Sent Events (SSE). This makes it incredibly lightweight and responsive.

For developers, this is perfect for several scenarios:

• Monitoring training runs for ML models without interrupting your terminal.
• Keeping an eye on GPU rendering jobs from another device on your local network.
• Quick diagnostics on a headless server by just navigating to its IP address and port.
• Having a persistent, at-a-glance view on a secondary monitor.

It's a focused tool that does one job well, without the bloat of a full system monitoring suite.

How to Try It

Getting started is straightforward. The project is on GitHub, and you can have it running in a few minutes.

1. Clone the repo:

   git clone https://github.com/psalias2006/gpu-hot.git
   cd gpu-hot
   

2. Install the single dependency (Flask):

   pip install flask
   

   (It's recommended to use a virtual environment.)

3. Run t